docs(plan): scope workstream G (cross-model behavior matrix)

Actionable scoping for the final workstream: a two-tier method (clean-room
first-turn harness generalised from the A2 repro-driver for cheap kickoff/resume
signals; sparing full Playwright F1 runs for the gate round-trip) over the
configured models (GLM 5.2 baseline, Deepseek V4, Qwen3.6, + breadth). Folds in
F's deferred single-select live check and A's resume-on-weaker-models check.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

docs/superpowers/plans/2026-06-30-cross-model-behavior-matrix.md

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+# Plan — Workstream G: cross-model behavior matrix
+
+Scoping doc (not yet executed). The UI/UX redesign workstreams D, E, B, C, F, A are done and on
+`main`. G is the final, separate piece: prove the model-facing workflow is robust across **any**
+configured model, not just GLM 5.2 — and close the two live checks deferred into G:
+
+- **F's live check** — does the model actually emit a single-pick `reviewer_select` carrying a
+  `decision` payload (auto-confirm), with **no** redundant follow-up gate, and the decision landing
+  in `review_decisions.jsonl`?
+- **A's resume robustness** — does each model chain into the resume bootstrap tool calls in-turn
+  (no narrate-and-stop), as GLM 5.2 now does on pi 0.79.4?
+
+## Why this is its own workstream
+
+A2 already showed model behavior is the variable that matters (the resume stall was a model/pi
+artifact, not a code bug). The gate contract (kickoffs, `reviewer_*` widgets, SKILL discipline) is
+model-agnostic by design, but only GLM 5.2 has been exercised live. Weaker/non-thinking models are
+the realistic failure surface: turn-dropping, narrate-and-stop, stringified-array tool args,
+ignoring the auto-confirm decision payload, choosing the wrong widget.
+
+## Models in play (from backend `/models`, 2026-06-30)
+
+| Plan name    | id(s)                                   | tier / notes |
+|--------------|-----------------------------------------|--------------|
+| GLM 5.2      | `zai/glm-5.2`                           | baseline (verified live); thinking |
+| Deepseek V4  | `deepseek/deepseek-v4-pro`, `…-flash`   | pro + a cheaper/faster flash |
+| Qwen3.6      | `aritmolab/qwen3.6-35b-a3b`             | local AritmoLab endpoint; MoE |
+| (others)     | `zai/glm-4.7`, `glm-5.1`, `gemma4-26b`  | breadth / regression coverage |
+
+Prioritise the three named primaries first (GLM 5.2 baseline, Deepseek V4 pro, Qwen3.6), then add
+one non-thinking / smaller model (gemma4 or glm-4.5-air) to probe the weak end.
+
+## Method — two tiers (cheap signal first, full run sparingly)
+
+**Tier 1 — clean-room first-turn harness (fast, ~15-30s/run, cheap).** Generalise the A2
+`repro-driver.mjs` (the proven clean-room driver: spawn `pi --mode rpc` with the backend env, send
+`set_model {provider,modelId}` → `set_thinking_level {level}` → `prompt`, capture raw JSONL,
+classify the first turn). Parameterise over `(model, thinking, scenario)` and record:
+`CHAINED_INTO_TOOLCALL` vs `STALLED_TURN_ENDED_IDLE`, time-to-first-tool, the first tool name, and
+the assistant-text tail. Scenarios that only need the FIRST turn:
+- **new-question kickoff** — first tool should be `read` (SKILL.md). (kickoff robustness)
+- **resume kickoff** — first tools should be `tht session show` + `read SKILL.md` in-turn. (A)
+
+**Tier 2 — full live run via Playwright (slow, ~3-4 min/F1, run sparingly).** Only for the
+end-to-end widget/persistence checks that need a real gate round-trip. Drive ONE canonical session
+per model partway through F1 and assert:
+- **single-select (F)** — the model emits `reviewer_select` whose chosen option carries a
+  `decision`; answering it persists ONE decision (`review_decisions.jsonl`) and shows **no**
+  follow-up confirmation widget; an "Altro"/"back" answer is still handled (non-persisting).
+- **multiselect** — a genuinely multi-answer ambiguity uses `reviewer_decide` (checkboxes).
+- **thinking vs non-thinking pacing** — note latency / whether non-thinking models skip steps.
+
+## What to record
+
+A matrix `(model × scenario) → {verdict, time-to-first-tool, widget/decision correctness, notes}`.
+Capture in PROJECT_STATE + a new `thothii-cross-model-matrix` memory. For every model that
+misbehaves, harden the **model-facing prompt** (kickoffs in `tht-gate.js`, discipline in
+`SKILL.md`) and re-run — never special-case per model in code; keep the contract uniform.
+
+## Deliverables
+
+1. A reusable matrix harness (promote the A2 driver out of scratchpad into e.g.
+   `harness/scripts/model-matrix.mjs`, committed).
+2. The filled results matrix (PROJECT_STATE + memory).
+3. Prompt hardening PRs where a model diverges, each re-verified.
+4. A short "supported models" note (which models drive the workflow reliably; which to avoid).
+
+## Risks / notes
+
+- **psd is the active workspace (real client data).** Use throwaway sessions + delete after (the A2
+  pattern); never drive turns on the user's real sessions.
+- **Cost/time:** Tier 1 is cheap; cap Tier 2 to one full F1 per model. Local AritmoLab models
+  (Qwen3.6, gemma4) may need the AritmoLab endpoint reachable (VPN) and may differ on tool-call
+  formatting — `prepareReviewerArguments` already parses stringified-array args, a known quirk.
+- **Non-thinking models** may need `thinking:"low"`/none; sweep the thinking level as a variable.
+- Strictly separate from the shipped workstreams: G changes prompts/docs only, never the gate's
+  control flow.